Results 61 to 70 of about 497,808 (317)

Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change

Remote Sensing, 2022
Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models.
Alysha van Duynhoven, Suzana Dragićević   +1 more
doaj   +1 more source

Combining Residual Networks with LSTMs for Lipreading

, 2017
We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks.
Stafylakis, Themos, Tzimiropoulos, Georgios   +1 more
core   +1 more source

Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma

Molecular Oncology, EarlyView.
This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu, Jianchao Wang, Fuqing Chen, Xuefeng Wang, Bin Lan, Ruyi Fu, Hong Wen, Fangfang Chen, Wei Hong, Tian‐Yu Tang, Ying He, Gang Chen, Jianyin Zhou, Hai‐Long Piao, Di Chen, Shu‐Yong Lin   +15 more
wiley   +1 more source

The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spatiotemporal fusion is a method of fusing high spatial resolution low temporal resolution remote sensing images and low spatial resolution high temporal resolution in order to obtain high spatiotemporal resolution remote sensing images, which can ...
ChenYang Weng, Yulin Zhan, Xingfa Gu, Jian Yang, Yan Liu, Hong Guo, Zilong Lian, Shiyuan Zhang, Zhangjie Wang, Xuechun Zhao   +9 more
doaj   +1 more source

Action Recognition Based on Two-Stream Convolutional Networks With Long-Short-Term Spatiotemporal Features

IEEE Access, 2020
Human action recognition is an important research topic in the field of computer vision due to its application values. Recently, a variety of approaches based on deep learning features have been proposed due to the effectiveness of deep neural networks ...
Yanqin Wan, Zujun Yu, Yao Wang, Xingxin Li   +3 more
doaj   +1 more source

Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective [PDF]

arXiv, 2023
Spatiotemporal learning, which aims at extracting spatiotemporal correlations from the collected spatiotemporal data, is a research hotspot in recent years. And considering the inherent graph structure of spatiotemporal data, recent works focus on capturing spatial dependencies by utilizing Graph Convolutional Networks (GCNs) to aggregate vertex ...
arxiv  

Collaborative Spatio-temporal Feature Learning for Video Action Recognition

, 2019
Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D).
Li, Chao, Pu, Shiliang, Xie, Di, Zhong, Qiaoyong   +3 more
core   +1 more source

Bioengineering facets of the tumor microenvironment in 3D tumor models: insights into cellular, biophysical and biochemical interactions

FEBS Open Bio, EarlyView.
The tumor microenvironment is a dynamic, multifaceted complex system of interdependent cellular, biochemical, and biophysical components. Three‐dimensional in vitro models of the tumor microenvironment enable a better understanding of these interactions and their impact on cancer progression and therapeutic resistance.
Salma T. Rafik, Deniz Bakkalci, Alexander J. MacRobert, Umber Cheema   +3 more
wiley   +1 more source

Examining Deep Learning Architectures for Crime Classification and Prediction

Forecasting, 2021
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep ...
Panagiotis Stalidis, Theodoros Semertzidis, Petros Daras   +2 more
doaj   +1 more source

Going Deeper into Action Recognition: A Survey

, 2017
Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation.
Harandi, Mehrtash, Herath, Samitha, Porikli, Fatih   +2 more
core   +1 more source